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Efficient Exploration

Efficient Exploration is one of the main obstacles in scaling up modern deep reinforcement learning algorithms. The main challenge in Efficient Exploration is the balance between exploiting current estimates, and gaining information about poorly understood states and actions.

Source: Randomized Value Functions via Multiplicative Normalizing Flows

Papers

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Showing 276–300 of 514 papers

TitleStatusHype
LECO: Learnable Episodic Count for Task-Specific Intrinsic RewardCode0
The Role of Coverage in Online Reinforcement Learning—0
Deterministic Sequencing of Exploration and Exploitation for Reinforcement Learning—0
Self-supervised Sequential Information Bottleneck for Robust Exploration in Deep Reinforcement Learning—0
An Empirical Evaluation of Posterior Sampling for Constrained Reinforcement LearningCode0
Impact Makes a Sound and Sound Makes an Impact: Sound Guides Representations and ExplorationsCode0
Learning Action Translator for Meta Reinforcement Learning on Sparse-Reward Tasks—0
SkexGen: Autoregressive Generation of CAD Construction Sequences with Disentangled Codebooks—0
The split Gibbs sampler revisited: improvements to its algorithmic structure and augmented target distributionCode0
GAN-based Intrinsic Exploration For Sample Efficient Reinforcement Learning—0
Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation—0
Scalable Exploration for Neural Online Learning to Rank with Perturbed Feedback—0
Sample-Efficient, Exploration-Based Policy Optimisation for Routing Problems—0
On Preemption and Learning in Stochastic SchedulingCode0
Personalized Algorithmic Recourse with Preference ElicitationCode0
SFP: State-free Priors for Exploration in Off-Policy Reinforcement Learning—0
Feature and Instance Joint Selection: A Reinforcement Learning Perspective—0
Fire Burns, Sword Cuts: Commonsense Inductive Bias for Exploration in Text-based GamesCode0
On Machine Learning-Driven Surrogates for Sound Transmission Loss SimulationsCode0
A Variational Approach to Bayesian Phylogenetic InferenceCode0
Efficient Exploration via First-Person Behavior Cloning Assisted Rapidly-Exploring Random Trees—0
TANDEM: Learning Joint Exploration and Decision Making with Tactile Sensors—0
Collaborative Training of Heterogeneous Reinforcement Learning Agents in Environments with Sparse Rewards: What and When to Share?Code0
Learning Causal Overhypotheses through Exploration in Children and Computational Models—0
A Unified Perspective on Value Backup and Exploration in Monte-Carlo Tree Search—0
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